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Comment by ofrzeta | Hacker News Reader

I came to say that this will probably make our fan constantly spin until I saw that this is already in the TODO list of the project.

Every client side feature such as background blur or face detection will make my (or every other person's I know) fans spin constantly so I will never use that feature.

I don't know if there's a potential software way to solve this problem or do we have to wait until everyone's using an m2 or i8 cpu?

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Camera resolution is likely to grow at the same rate as processor improvements. Downsizing stream first might be the best bet.

Although, if this is meant for prototypes, it doesn't matter if you get to validate your idea's efficacy

Image size is already downscaled to 450 * 380, and afaik most ML libraries downscale the input by default. In that context a higher camera resolutions only really affect noise levels due to the effective super-sampling, not the performance of the library (assuming downscaling is an insignificant relative to the hand detection part).

(I get about 28-30fps on my M1 MBP, didn't get any fan spin but wasn't using it for long).

What browser did you use? It's interesting because the M1 is so new and I wonder about JS engine optimizations on various browsers.

This was the Apple Silicon optimised Chrome. I tried Firefox on my desktop (9900k) and it have awful results, like 1fps. Downloaded chrome specially for this, and haven’t yet tried on Safari. I’ll do so now.

EDIT: 23-24FPS under Safari on M1.

I'm not hip to the js world. Is there a common pattern for offloading the compute heavy parts of a js workload to a server somewhere? I'd totally just leave my pc running so it could do the heavy lifting for my mobile devices if that meant better battery life.

If there's going to be waste heat, I'd rather it go into the building that I pay to heat.

They don't need to be offloaded to a server, but just optimized: either running in web assembly or running on special machine learning cores (like on Apple devices).

The ability to run deep learning inference on cuda, opencl or whatever the browser has access to would be a _BIG_ leap forward to ai in the browser.

Maybe an onnx native binding should be available in browsers and the browser can choose the most performant execution engine at runtime.

This is running on the GPU. It's using a WebGL-oriented version of tensorflow. The problem is most likely the sorry state of GPU drivers on macOS, causing your system config to be disallowlisted against having hardware acceleration WebGL enabled by default.

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